Hybrid optimization in loop: 3D human body reconstruction from basic information

Abstract

3D human reconstruction is widely used for digital transformation in different industries such as e-retail, entertainment, health care, epidemiology. For practical applicability, the modeling process is required to be quick, affordable while still ensuring capabilities in reconstruction accuracy and reliability. To meet such business requirements, we propose a novel technique for producing an exact 3D human body using only basic anthropomorphic measurements. To begin, the paper refers to and summarizes core technologies of the three most common 3D human reconstruction approaches, including (1) Using Point Clouds, (2) Using Images, and (3) Using Anthropometric Measurements. Despite successfully recreating 3D human shapes, these methods face problems of long processing time and high investment cost, making the solution impractical for mass use. Moreover, in the human reconstruction sector particularly, the variety of human shapes, poses, and clothing poses a significant challenge to output accuracy. In this regard, our method combines (1) a local optimization model for determining hyperparameters for classifying different human shapes and (2) a global optimization for reconstructing 3D models, allowing reconstruction of both naked human bodies and clothed ones. The proposed method was evaluated quantitatively and qualitatively using a real dataset to demonstrate its feasibility and efficiency when used in real-world applications.